Method for predicting the risk of deep vein thrombosis and pulmonary embolism associated with hormonal preparations and hormone levels

ABSTRACT

Specific single nucleotide polymorphisms (SNPs) in the human genome, and their association with deep vein thrombosis (DVT) and related pathologies, such as pulmonary embolism (PE), in relation with hormonal preparations (i.e. combined contraceptives, hormone replacement therapeutics) and hormone levels (i.e. during pregnancy and post-partum).

TECHNICAL FIELD

The invention relates to the field of medicine, in particular to thefield of blood clotting diseases prognosis. Specifically, the presentinvention relates to specific single nucleotide polymorphisms (SNPs) inthe human genome, and their association with deep vein thrombosis (DVT)and related pathologies, such as pulmonary embolism (PE), in relationwith hormonal preparations (e.g. combined contraceptives, or hormonereplacement therapies) and hormone levels (e.g. during pregnancy, orpost-partum).

BACKGROUND OF THE INVENTION

Venous thrombosis is a serious medical condition that occurs when ablood clot (thrombus) forms in one or more veins of the body. Oneparticular form of venous thrombosis is called deep vein thrombosis whenthe blood clot occurs in deep veins. Such blood clots might travelthrough the bloodstream and lodge in lungs, where they can block bloodflow, causing pulmonary embolism. Several inherited or acquiredconditions can increase coagulability of blood and thus the tendency todevelop blood clots. The inherited conditions include mutations in thediverse well-known clotting, anticoagulant, or thrombolytic factors,such as the factor V Leiden mutation (in the factor V gene), mutationsin the prothrombin gene (factor II), or in the methylenetetrahydrofolatereductase gene (MTHFR). Additional mutations can be present in genescoding for protein C and protein S although they are rare. Other likelyinherited causes include a possible increase in the expression ofprocoagulant factors such as factor VIII, von Willebrand factor, andfactors IX and XI (Cushman M, (2005), Hematology Am Soc Hematol EducProgram: 452-457). Examples of the acquired conditions that can causeDVT are surgery and trauma, prolonged immobilization, cancer,myeloproliferative disorders, and even pregnancy and post-partum(Seligsohn U. and Lubetsky A., (2001) New Eng J Med 344(16): 1222-1231).DVT might occur as the result of a genetic mutation alone or in concertwith behavioural and environmental factors, such as prolongedimmobilisation, smoking and hormonal treatments. Thus, DVT is consideredcomplex or multifactorial disease.

Over 100 million women worldwide use combined estroprogestativecontraceptives (CC), due to their very high effectiveness in reducingthe risk of unwanted pregnancy and their beneficial effect on diversesymptoms related to women's cycle. Nonetheless, these contraceptivesalso increase the risk of blood clotting substantially, which canultimately lead to DVT and PE (Vinogradova Y., Coupland C. andHippisley-Cox J., (2015) BMJ 350: h2135). Newer generations of the CC,the so-called 3rd and 4th generation CC, are usually better tolerated bywomen but importantly they increase the risk of developing DVT even morethan the older preparations of the so-called 2^(nd) and 1^(st)generations.

The incidence of thrombosis among CC users is around 1‰, which is 10times more than in population in general of the same age. In Francealone, where over 3 M women aged 15-49 use CC, the National Agency forthe Safety of Drugs and Health Products reports every year over 2′500cases of DVT, 850 cases of PE, and 20 cases of death linked tocontraceptive pills. According to recent estimates, in Europe, 22′000DVT cases related to CC occur each year. Thus, one of the majorchallenges for healthcare professionals is to identify women at risk ofdeveloping DVT related to CC, and advise them on alternativecontraception methods.

As the standard of care nowadays, prescribing physicians use a medicalquestionnaire to assess the risk of thrombosis, mostly focusing on age,body mass index and smoking habits that are known risk factors fordisease development, as well as on the personal and familial history ofDVT or related diseases. If the familial or personal history ispositive, physicians test for the thrombophilic status of the patient(complete or partial blood analysis including two genetic risk factors:the factor V-Leiden and the prothrombin mutations). Taking into accountthe currently registered number of thrombosis cases related to CC, thisapproach is a relatively low performing one, with suboptimal sensitivityand specificity. This observation is futher confirmed through diversestudies that demonstrate that these informations, notably familialhistory, are insufficient to reliably estimate risk of DVT (de Haan H.G. et al., (2012), Blood 120: 656-663; Suchon P, et al., (2015),Thrombosis and Haemostasis 114(6)).

Different studies have tried to combine further genetic and clinicalparameters to predict individuals at risk of thrombosis (de Haan H.G. etal., (2012) Blood 120: 656-663, Bruzelius, et al., (2015) J ThrombHaemost. 13(2):219-27). Nonetheless, these approaches were not focusedat women undergoing hormonal changes, but included all kind of patientsthat developed thrombosis (e.g. cancer patients, patients undergoingimmobilisation, surgery, etc). Thus, the impact of these models inpredicting risk of thrombosis for women undergoing hormonal changesremained unclear until now.

Hormone replacement therapy for menopause (HRT) aims to preventdiscomfort caused by diminished circulating estrogen and progesteronehormones in woman's body, or in the case of the surgically orprematurely menopaused women, it aims at prolonging life and reducingthe occurence of osteophorosis and dementia. It involves use ofpreparations that usually include estrogens and progesterone orprogestins. As in the case of contraceptive treatments, these hormonesand consequently the replacement treatments increase importantly therisk of developing DVT and PE. It is now well established that thepresence of the factor V Leiden mutation and prothrombin mutation has amultiplicative effect on the overal risk of DVT related to homonereplacement therapy (Douketis J. D. et al., (2011), Clin Appl ThrombHemost. 17(6): E106-113; Botto N., et al., (2011), Climacteric. 14(1):25-30). Nonetheless, there are no precise methods available to estimatethe risk of DVT related to HRT use.

Thus, considering women subjects undergoing a change in hormone levels,there remains significant unmet need to develop a prognostic method foridentifying if a woman is at risk of developing a blood clotting diseaseand to develop a method for calculating such risk with improvedsensitivity and specificity.

SUMMARY OF THE INVENTION

The present invention provides a prognostic method for identifying if awoman subject undergoing a change in hormone levels is at risk ofdeveloping a blood clotting disease, the method comprising the steps of:

a) Determining in a sample from said woman subject the genotype ofsingle nucleotide polymorphism of rs1799853 (SEQ ID NO:1), rs4379368(SEQ ID NO:2), rs6025 (SEQ ID NO:3), rs1799963 (SEQ ID NO:4), rs8176719(SEQ ID NO:5), rs8176750 (SEQ ID NO:6), rs9574 (SEQ ID NO:7), rs2289252(SEQ ID NO:8), and rs710446 (SEQ ID NO:9);

b) Determining the clinical risk factors of said woman subject, saidclinical risk factors are selected from the group comprising the smokingstatus, the BMI, the age, the familial history of blood clottingdiseases and the change in hormone levels;

c) Combining the genotyping data of step a) and the clinical riskfactors of step b) on a decision support algorithm that gives a riskscore; and

d) Analysing the risk score in order to determine the risk of said womansubject to develop a blood clotting disease.

The invention also provides an apparatus for calculating an estimationvalue of the risk of developing a blood clotting disease in a womansubject undergoing a change in hormone levels based on the womansubject-specific input features, said apparatus comprising:

a) a data interface for receiving said input features;

b) a processor for calculating said estimation value by applying adecision support algorithm as a function of numerical values derivedfrom said received input features; and

c) a user interface for outputting said estimation value; wherein saidinput features include a combination of:

-   -   (i) the genotype of single nucleotide polymorphism of rs1799853        (SEQ ID NO:1), rs4379368 (SEQ ID NO:2), rs6025 (SEQ ID NO:3),        rs1799963 (SEQ ID NO:4), rs8176719 (SEQ ID NO:5), rs8176750 (SEQ        ID NO:6), rs9574 (SEQ ID NO:7), rs2289252 (SEQ ID NO:8), and        rs710446 (SEQ ID NO:9); and    -   (ii) the clinical risk factors comprising the smoking status,        the BMI, the age, the familial history of blood clotting        diseases and the change in hormone levels of said woman subject.

Also provided is a method for calculating an estimation value of therisk of developing a blood clotting disease in a woman subjectundergoing a change in hormone levels based on woman subject-specificinput features, said method comprising:

a) selecting said input features to include a combination of:

-   -   (i) the genotype of single nucleotide polymorphism of rs1799853        (SEQ ID NO:1), rs4379368 (SEQ ID NO:2), rs6025 (SEQ ID NO:3),        rs1799963 (SEQ ID NO:4), rs8176719 (SEQ ID NO:5), rs8176750 (SEQ        ID NO:6), rs9574 (SEQ ID NO:7), rs2289252 (SEQ ID NO:8), and        rs710446 (SEQ ID NO:9); and    -   (ii) the clinical risk factors comprising the smoking status,        the BMI, the age, the familial history of blood clotting        diseases and the change in hormone levels of said woman patient;        and

b) calculating said estimation value by applying a decision supportalgorithm as a function of numerical values derived from said receivedinput features.

The invention further provides a kit for use in identifying if a womansubject undergoing a change in hormone levels is having a risk ofdeveloping a blood clotting disease, said kit comprising

i) at least one detection reagent for detecting the genotype of singlenucleotide polymorphism of rs1799853 (SEQ ID NO:1), rs4379368 (SEQ IDNO:2), rs6025 (SEQ ID NO:3), rs1799963 (SEQ ID NO:4), rs8176719 (SEQ IDNO:5), rs8176750 (SEQ ID NO:6), rs9574 (SEQ ID NO:7), rs2289252 (SEQ IDNO:8), and rs710446 (SEQ ID NO:9); and optionally

ii) instructions for use.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: Pill Protect® (PP) risk score distribution for 1622 women onhormonal contraceptives. woTEV=controls, women who have not developedthrombosis. With TEV=cases, women who have developed thrombosis.

FIG. 2: ROC curves for PP (full line), MD (dotted line) and MDg(straight dashed line) scores among the 1622 women on hormonalcontraceptives.

FIG. 3: Pill Protect® (PP) score distribution for 26 women eligible forHRT. woTEV=controls women who have not developed thrombosis. WithTEV=subjects who have developed thrombosis.

DETAILED DESCRIPTION OF THE INVENTION

Although methods and materials similar or equivalent to those describedherein can be used in the practice or testing of the present invention,suitable methods and materials are described below. All publications,patent applications, patents, and other references mentioned herein areincorporated by reference in their entirety. The publications andapplications discussed herein are provided solely for their disclosureprior to the filing date of the present application. Nothing herein isto be construed as an admission that the present invention is notentitled to antedate such publication by virtue of prior invention. Inaddition, the materials, methods, and examples are illustrative only andare not intended to be limiting.

In the case of conflict, the present specification, includingdefinitions, will control. Unless defined otherwise, all technical andscientific terms used herein have the same meaning as is commonlyunderstood by one of skill in art to which the subject matter hereinbelongs. As used herein, the following definitions are supplied in orderto facilitate the understanding of the present invention.

Other objects and advantages of the invention will become apparent tothose skilled in the art from a review of the ensuing detaileddescription, which proceeds with reference to the following illustrativedrawings, and the attendant claims.

The term “comprise” is generally used in the sense of include, that isto say permitting the presence of one or more features or components.

As used in the specification and claims, the singular forms “a”, “an”and “the” include plural references unless the context clearly dictatesotherwise.

As used herein the terms “subject” or “patient” are well-recognized inthe art, and, are used interchangeably herein to refer to a “womanundergoing a change in hormone levels”, in particular female hormonelevels, either induced by a treatment (for example contraceptives and/orhormone replacement therapy) or naturally occurring (for examplepregnancy and postpartum). In some embodiments, the subject is a subjectin need of treatment or a subject with a disease or disorder. However,in other embodiments, the subject can be a normal woman subject. Theterm does not denote a particular age.

The present invention relates to the identification of novel singlenucleotide polymorphisms (SNPs) and unique combinations of such SNPs, aswell as combination of SNPs and clinical risk factors (behavioural andenvironmental factors) that are associated with risk of developing bloodclotting diseases such as deep vein thrombosis (DVT) and Pulmonaryembolism (PE) for women subjects undergoing a change in hormone levels.

The present invention provides a prognostic method for identifying if awoman subject undergoing a change in hormone levels is at risk ofdeveloping a blood clotting disease, the method comprising the steps of:

a) Determining in a sample from said woman subject the genotype ofsingle nucleotide polymorphism of rs1799853 (SEQ ID NO:1), rs4379368(SEQ ID NO:2), rs6025 (SEQ ID NO:3), rs1799963 (SEQ ID NO:4), rs8176719(SEQ ID NO:5), rs8176750 (SEQ ID NO:6), rs9574 (SEQ ID NO:7), rs2289252(SEQ ID NO:8), and rs710446 (SEQ ID NO:9);

b) Determining the clinical risk factors of said woman subject, saidclinical risk factors are selected from the group comprising the smokingstatus, the BMI, the age, the familial history of blood clottingdiseases and the change in hormone levels;

c) Combining the genotyping data of step a) and the clinical riskfactors of step b) on a decision support algorithm that gives a riskscore; and

d) Analysing the risk score in order to determine the risk of said womansubject to develop a blood clotting disease.

As used herein, the term “blood clotting disease” refers to diseasesselected from the group comprising vein thrombosis, deep vein thrombosis(DVT), pulmonary embolism (PE) and arterial thrombosis.

Preferably, the present invention provides a prognostic method foridentifying if a woman subject undergoing a change in hormone levels isat risk of developing a deep vein thrombosis and/or a pulmonaryembolism.

As used herein the term “change in hormone levels” refers to anytreatment that involves hormone and/or particular change in a woman'slife that modifies hormone levels. In particular, treatments includecombined contraceptives, progestin-only contraceptives, hormonereplacement therapy, assisted reproductive technology, pregnancy andpostpartum periods.

“Combined contraceptive” refers to any contraceptives that contain anestrogen combined with a progestin.

Diverse forms of variations in a DNA sequence exist. Single nuleotidepolymorphisms (SNPs) are the most common ones, representing more than90% of all differences among individuals. Precisely, a SNP is a commonlyoccurring DNA variation (e.g. with frequency higher than 1%) in whichtwo chromosomes differ in a given segment a single position (a singlebase pair). Though SNPs are naturally and commonly occurring variationsin human DNA, if they are part of the coding or regulatory DNAsequences, they might actually alter the expression of genes orstability of its transcripts and thus confer some advantages and risk tothe carriers. It is now well established that these common geneticvariants or their combination might be associated to some major traits,such as diseases.

The prognostic method according to the invention comprises the steps ofdetermining in a sample from a woman subject undergoing a change inhormone levels the genotype of at least one single nucleotidepolymorphism (SNP) selected from the group comprising:

TABLE 1 rs6120849 rs2289252 rs9380643 rs1039084 rs11210892 rs1801131rs2036914 rs169713 rs169715 rs7082872 rs5742904 rs670659 rs2288904rs1801133 rs3136516 rs1800790 rs4680 rs5985 rs4981021 rs1063856rs1613662 rs8176747 rs867186 rs9574 rs4524 rs6025 rs13146272 rs7412rs1799963 rs10133762 rs1593812 rs710446 rs1799889 rs1884841 rs2227589rs9390459 rs429358 rs8176719 rs1799853 rs3813948 rs1053878 rs4379368rs8176750 rs2228220 rs2066865 rs5918 rs10029715 rs1800595

Further SNPs can be determined based on the SNPs described in thepresent invention by statistical correlation. “Linkage disequilibrium”(LD) refers to the statistical correlation between two neighboring SNPs.LD is generally quantified with either Lewontin's parameter ofassociation (Lewontin, R. C. (1964). Genetics. 49 (1): 49-67) or withthe r² parameter based on Pearson correlation coefficient (Karl Pearson(1895) Proceedings of the Royal Society of London, 58: 240-242). Whenthe LD value is equal to 1, the two SNPs are in complete disequilibrium.In contrast, two SNPs with a LD value equal to 0 are in complete linkageequilibrium. Linkage disequilibrium is calculated following theapplication of the expectation maximization algorithm (EM) for theestimation of haplotype frequencies.

Thus, in the present invention, SNPs considered in LD with rs1799853(SEQ ID NO: 1), rs4379368 (SEQ ID NO: 2), rs6025 (SEQ ID NO: 3),rs1799963 (SEQ ID NO: 4), rs8176719 (SEQ ID NO: 5), rs8176750 (SEQ IDNO: 6), rs9574 (SEQ ID NO: 7), rs2289252 (SEQ ID NO: 8), or rs710446(SEQ ID NO: 9) are SNPs with a r² greater than 0.5.

Preferably, the prognostic method according to the invention comprisesthe step of determining in a sample from a woman subject undergoing achange in hormone levels the genotype of single nucleotide polymorphismof rs6025 (SEQ ID NO:3); rs1799963 (SEQ ID NO:4); rs8176719 (SEQ IDNO:5); rs8176750 (SEQ ID NO:6); rs9574 (SEQ ID NO:7); rs2289252 (SEQ IDNO:8); rs710446 (SEQ ID NO:9); rs4379368 (SEQ ID NO:2); rs1799853 (SEQID NO:1).

TABLE 2 SEQ ID NO: rs Sequence Gene SEQ ID NO: 1 rs1799853GATGGGGAAGAGGAGCA CYP2C9 gene TTGAGGAC[C/T]GTGTTCA (NG_008385)AGAGGAAGCCCGCTGCCT SEQ ID NO: 2 rs4379368 TGGATGGTATTGACTTTTA SUGCT geneCATCAC[C/T]GAAGGTGTT (NG_023422) TCCATAGATGGAAGAC SEQ ID NO: 3 rs6025TGTAAGAGCAGATCCCTG Factor V gene GACAGGC[A/G]AGGAATA (NG_011806)CAGGTATTTTGTCCTTGA SEQ ID NO: 4 rs1799963 GTTCCCAATAAAAGTGACFactor II gene TCTCAGY[A/G]AGCCTCAA (NG_008953) TGCTCCCAGTGCTATTCSEQ ID NO: 5 rs8176719 GCAGTAGGAAGGATGTCC ABO gene (NG_006669)TCGTGGT[-/G]ACCCCTTG GCTGGCTCCCATTGTCT SEQ ID NO: 6 rs8176750CCAAGAACCACCAGGCGG ABO gene (NG_006669) TCCGGAA[-/C]CCGTGAGCGGCTGCCAGGGGCTCTG SEQ ID NO: 7 rs9574 GCGATGTTAATTACTCTCC PROCR geneAGCCCC[C/G]TCAGAAGG (NG_032899) GGCTGGATTGATGGAGG SEQ ID NO: 8 rs2289252GTGAGGGTGAGGCTTGTC Factor 11 gene TCTCT[C/T]GCCCTCTCA (NG_008051)TCCTGGCACATGTGCG SEQ ID NO: 9 rs710446 AGGGATCCAATCGTCATCKNG1 gene (NG_016009) ACTCTGT[A/G]TGGGAGCT GGTGATATAGGAGGCAT

The prognostic method according to the invention also comprises the stepof determining the clinical risk factors of said woman subject, saidclinical risk factors are selected from the group comprising the smokingstatus, the BMI, the age, the familial history of blood clottingdiseases, the change in hormone levels, the personal history of bloodclotting diseases (such as DVT and/or PE), the alcohol status, apersonal history of hypertension, of cholesterol, of diabetes, ofautoimmune disease, of cancer and of other cardiovascular diseases, thehistory of contraception, the duration of the contraception, and theconcomitant use of other drugs.

Preferably, the clinical risk factors are selected from the groupconsisting of the smoking status, the BMI, the age, the familial historyof blood clotting diseases and/or the change in hormone levels.

A value (variable x_(n)) is assigned for each clinical factors and isused to calculate the score Pill Protect® (PP). Values of variablesx_(n) are as follows:

-   -   Smoking status factor: a value of 0 is accorded to a non-smoking        status, a value of 1 is accorded to a smoking status.    -   BMI (Body Mass Index) factor: the value corresponds to the BMI        of the subject, calculated according to the weight and the        height of the subject.    -   Age factor: the value corresponds to the age of the subject.    -   Familial history factor of DVT and/or PE: a value of 0 is        accorded in absence of blood clotting diseases in the familial        history, and a value of 1 is accorded in presence of blood        clotting diseases in the familial history.    -   Change in hormone level: the value is dependent on the cause of        change in hormone level.

For example, the type of progestin included in the combinedcontraceptive is taken into account.

Clinical risk factors as disclosed herein may be determined through therequest form sent by the physician or healthcare provider.

In the prognostic method according to the invention, the genotype of SNPis determined for example by nucleic acid sequencing and/or by PCRanalysis in a sample of the woman subject undergoing a change in hormonelevels.

The sample may be any biological sample, containing DNA and derived fromthe subject. This includes body fluids, tissues, cells, biopsies and soon. The preferred samples are saliva and blood.

The sample is collected according to the transfer method of choice andis treated to purify nucleic acids. These treatments include lysis,centrifugation and washing steps. The lysis includes mechanical,physical or chemical approaches. The purified nucleic acid is then usedto genotype the above SNPs. The nucleic acid is added to a buffer,enzymes, specific primers and/or probes. This can be done in anysuitable device such as tube, plate, well, glass etc. The genotype ofthe above SNPs may be detected by PCR, RFLP, allele-specific PCR,quantitative PCR, sequencing, microarray, hybridization, etc.

Sequencing can be performed using automatic sequencers using varioustechniques and following state of the art protocols. The sequencingreactions can be performed on complete genes or on specific regionscovering the SNPs. For standard sequencing, the reaction requires apiece of DNA that initiates the amplification reaction, standardnucleotides and modified nucleotides that block the amplificationreaction. Next generation sequencing can be performed by varioustechniques that massively amplified DNA regions.

Amplification can be performed using numerous techniques such aspolymerase chain reaction (PCR), allele-specific PCR (ASA-PCR), PCRfollowed by a restriction enzyme digestion (RFLP-PCR) and quantitativePCR. These techniques are based on the amplification of specific regionsusing small pieces of DNA that hybridize to the specific regions andinitiate the amplification reaction. The reaction also requiresnecessary components to make the amplification work such as nucleotidesand enzymes. In the case of ASA-PCR, a multiplex reaction or independentreactions are used by mixing 4 pieces of DNA that will initiate theamplification reaction. Among these 4 pieces of DNA, 2 are specific tothe SNP; one will be specific to the effect allele and the other will bespecific to the non-effect allele. The sizes of the amplified regionswould be different to be able to distinguish between them on a regularagarose gel. In the case of RFLP-PCR, the amplified material is treatedusing a restriction enzyme to cut the amplified region according to theSNP allele. For example, enzyme A would cut the effect allele, while itwould not cut the non-effect allele. The sizes of the digested amplifiedregion are distinguished on a regular agarose gel. For quantitative PCR,labelled probes specific to each allele are added to the amplificationreaction to distinguish between the two alleles.

Genotyping can also be performed using hybridization techniques on asolid support or in suspension. These techniques are based on thehybridization of material amplified with allele specific probes andprimers onto a support. The amplified material is labelled todistinguish the different alleles.

In the present invention, the prognostic method comprises the step ofcombining the genotyping data of step a) and the clinical risk factorsof step b) on a decision support algorithm that gives a risk score, saidrisk score is calculated by the steps of:

(i) Allocating in step a) a value of 2 for subjects that are homozygousfor the effect allele, a value of 1 for subjects that are heterozygousfor both alleles and a value of 0 for subjects that are homozygous forthe non-effect allele in the SNPs of rs1799853 (SEQ ID NO:1), rs4379368(SEQ ID NO:2), rs6025 (SEQ ID NO:3), rs1799963 (SEQ ID NO:4), rs8176719(SEQ ID NO:5), rs8176750 (SEQ ID NO:6), rs9574 (SEQ ID NO:7), rs2289252(SEQ ID NO:8), and rs710446 (SEQ ID NO:9);

(ii) Allocating in step b) a correlating value for the smoking status,the BMI, the age, the familial history of blood clotting diseases;

(iii) Calculating the Pill Protect® score (PP)

(iv) Calculating the absolute risk of the patient (AR)

(v) Analysing the PP and AR risk scores to determine the risk of thepatient to develop a blood clotting disease.

The highest naturally occurring risk of thrombosis for woman is duringthe period of post-partum. As the risk of thrombosis always exists evenwithout any hormonal change, the risk calculated by the prognosticmethod of the invention is compared with the risk of woman during thepost-partum period.

A score PP>20 is indicative that said woman subject has a risk as highas the natural risk during postpartum period of having or developing ablood clotting disease.

The score Pill Protect® (PP) is calculated as follows:

PP=exp(β₀+β₁ x ₁+ . . . +β_(n) x _(n))/exp(β₀+β₁ x _(1st)+ . . . +β_(n)x _(nst))

wherein:

β₀=coefficient linked to the risk to develop the disease not related tothe variables 1 to n.

β₁=regression coefficient that correlates with the risk to developthrombosis associated with the variable 1. The coefficient can be from−∞ to +∞.

x₁=value taken by the variable 1. The range of possible values dependson the variable.

β_(n)=regression coefficient that correlates with the risk to developthrombosis associated with the variable n. the coefficient can be from−∞ to +∞.

x_(n)=value taken by the variable n. The range of possible valuesdepends on the variable.

x_(1st)=value taken by the variable 1 for a standard woman. The valuedepends on the variable.

x_(nst)=value taken by the variable n for a standard woman. The valuedepends on the variable.

The standard woman corresponds to a woman with a BMI of 23, age 20, shedoes not smoke, has no familial history of thrombosis and has the mostfrequent allele for each SNP (the most frequent allele corresponds tothe non-effect allele for all SNPs except for rs9574 which is the effectallele (G;G)).

In particular, the genotyping data of step (i) include threepossibilities which are homozygous for the “effect-allele”, heterozygousor homozygous for the “allele with no effect”.

Table 3 gives the detailed genotypes for each SNP (A: Adenine, G:Guanine, C: Cytosine, T: Thymine, −: deletion).

The BMI and age are used as continuous variables. Smoking status andfamilial history are binary variables.

TABLE 3 Genotype Homozygous for the Homozygous for the SNP “effectallele” Heterozygous “non-effect allele” rs1799853 (T; T) (C; T) (C; C)rs4379368 (T; T) (C; T) (C; C) rs6025 (A; A) (A; G) (G; G) rs1799963 (A;A) (A; G) (G; G) rs8176719 (G; G) (−; G) (−; −) rs8176750 (−; −) (−; C)(C; C) rs9574 (G; G) (C; G) (C; C) rs2289252 (T; T) (C; T) (C; C)rs710446 (G; G) (A; G) (A; A) value of 2 1 0 variable x_(n)

As used herein, the term “effect allele” refers to the allele thatconfers the risk to develop thromboembolic disease and/or the protectiveeffect to develop a blood clotting disease to a woman subject undergoinga change in hormone levels.

As used herein, the term “non-effect allele” refers to the allele thatdoes not confer a risk or a protective effect to develop a bloodclotting disease to a woman subject undergoing a change in hormonelevels.

In particular, the type of hormone present in the subject's treatment isalso taken into account as a variable x. It includes the progestins:levonorgestrel, norgestimate, gestoden, desogestrel, drospirenone,dienogest, cyproterone acetate, in particular but not exclusively andalso the estrogens estradiol, ethinylestradiol, estradiol acetate,estradiol cypionate, estradiol valerate, estradiol enanthate, estradiolbenzoate, estradiol hemihydrate, in particular but not exclusively. Italso includes progestin-only pills, intrauterine devices and hormonereplacement therapy according to the route of administration such asoral or transdermal patch and other therapy such as Livial (Tibolone).

The absolute risk takes into account the incidence of blood clottingdevelopment according to the age range. The absolute risk (AR) iscalculated as follows:

AR=adjPP*the incidence according to the subject's age

wherein:

adjPP is calculated as for PP but the age variable for the standardwoman would be adjusted for the same age as the patient.

“the incidence according to the subject's age” is correlated to 0.5 fora subject of 15-20 years old, 2.5 for a subject of 20-30 years old, 3.5for a subject of 30-40 years old, 5.5 for a subject of 40-50 years old,10 for a subject of 50-60 years old., 50 for a subject of 60-75 yearsold, 100 for a subject of >75 years old (table 5);

As used herein, the “incidence” according to the subject's age has beendetermined based on the existing literature (Lidegraad O, BMJ, 2011,Oger E, Thromb Haemost, 2000, Silverstein R L, Blood 2007). Table 4lists the used incidence for 10′000 women per year.

TABLE 4 Age Incidence value 15-20 y.o. 0.5 20-30 y.o. 2.5 30-40 y.o. 3.540-50 y.o. 5.5 50-60 y.o. 10 60-75 y.o. 50   >75 y.o. 100

The invention also provides an apparatus for calculating an estimationvalue of the risk of developing a blood clotting disease in a womansubject undergoing a change in hormone levels based on the womansubject-specific input features, said apparatus comprising:

a) a data interface for receiving said input features;

b) a processor for calculating said estimation value by applying adecision support algorithm as a function of numerical values derivedfrom said received input features; and

c) a user interface for outputting said estimation value;

wherein said input features include a combination of:

-   -   (i) the genotype of single nucleotide polymorphism of rs1799853        (SEQ ID NO:1), rs4379368 (SEQ ID NO:2), rs6025 (SEQ ID NO:3),        rs1799963 (SEQ ID NO:4), rs8176719 (SEQ ID NO:5), rs8176750 (SEQ        ID NO:6), rs9574 (SEQ ID NO:7), rs2289252 (SEQ ID NO:8), and        rs710446 (SEQ ID NO:9); and    -   (ii) the clinical risk factors comprising the smoking status,        the BMI, the age, the familial history of blood clotting        diseases and the change in hormone levels of said woman subject.

Also provided is a method for calculating an estimation value of therisk of developing a blood clotting disease in a woman subjectundergoing a change in hormone levels based on woman subject-specificinput features, said method comprising:

a) selecting said input features to include a combination of:

-   -   (i) the genotype of single nucleotide polymorphism of rs1799853        (SEQ ID NO:1), rs4379368 (SEQ ID NO:2), rs6025 (SEQ ID NO:3),        rs1799963 (SEQ ID NO:4), rs8176719 (SEQ ID NO:5), rs8176750 (SEQ        ID NO:6), rs9574 (SEQ ID NO:7), rs2289252 (SEQ ID NO:8), and        rs710446 (SEQ ID NO:9); and    -   (ii) the clinical risk factors comprising the smoking status,        the BMI, the age, the familial history of blood clotting        diseases and the change in hormone levels of said woman patient;        and

b) calculating said estimation value by applying a decision supportalgorithm as a function of numerical values derived from said receivedinput features.

Preferably, said method further comprises optimizing said input featuresby a learning process based on a stored dataset of a plurality of womansubjects to minimize a prediction error.

As shown in the example, the estimation of the risk of developing ablood clotting disease in a woman subject undergoing a change in hormonelevels is very significant (example 1, FIG. 1 and example 2, FIG. 3) andthe performance of the method was improved compared to conventionalmethods (example 1, FIG. 2 and example 2, Table 8).

The invention further provides a kit for use in identifying if a womansubject undergoing a change in hormone levels is having a risk ofdeveloping a blood clotting disease, said kit comprising

i) at least one detection reagent for detecting the genotype of singlenucleotide polymorphism of rs1799853 (SEQ ID NO:1), rs4379368 (SEQ IDNO:2), rs6025 (SEQ ID NO:3), rs1799963 (SEQ ID NO:4), rs8176719 (SEQ IDNO:5), rs8176750 (SEQ ID NO:6), rs9574 (SEQ ID NO:7), rs2289252 (SEQ IDNO:8), and rs710446 (SEQ ID NO:9); and optionally

ii) instructions for use.

The kit contain necessary components to genotype the SNPs selected fromthe group comprising rs6025, rs1799963, rs8176719, rs8176750, rs9574,rs2289252, rs710446, rs4379368 and rs1799853. These components includeprimers and/or probes specific to each SNP. The primers are necessary toinitiate the amplification of each specific region corresponding to eachSNP. The probes hybridize specifically to each allele of each SNP andallow the detection of each allele. The probes for each allele can belabelled with different fluorophores to allow distinct detection. Thekit can also contain solid support to hybridize amplified material andallow the detection of each allele on the solid support after labellingreaction. The kit can also covers necessary reagents to sequence theregions around the 9 SNPs listed above or the full corresponding genes.These reagents include specific primers for each primer that would allowto initiate the amplification reaction and modified nucleotides.

Preferably, the SNP detection reagent is an isolated or synthetic DNAoligonucleotide probe or primer, or a RNA oligonucleotide or primer or aPNA oligomer or a combination thereof, that hybridizes to a fragment ofa target nucleic acid molecule containing one of the SNPs specified inany one of SEQ ID Nos. 1 to 9, or a complement thereof.

In particular, said SNP detection reagent can differentiate betweennucleic acids having a particular nucleotide at a target SNP position.

In the present invention, the SNP detection reagent hybridizes understringent conditions to at least 8, 10, 12, 16, 18, 20, 22, 25, 30, 40,50, 55, 60, 65, 70, 80, 90, 100, 120 or more consecutive nucleotides ina target nucleic acid molecule comprising at least one of the SNPsspecified in any one of SEQ ID Nos. 1 to 9, or a complement thereof.

Preferably, according to the invention, at least one SNP detectionreagent in the kit is an oligonucleotide or primer having a length of atleast 8 nucleotides, preferably a length of at least 10, 12, 16, 17, 18,19, 20, 21, 22, 23, 24 or 25 nucleotides.

The invention also relates to a kit for use, wherein the SNP detectionreagent is a compound that is labelled.

In another separate embodiment, the present invention relates to aprognostic method for identifying if a woman subject undergoing a changein hormone levels is having a risk of developing a blood clottingdisease, the method comprising:

-   -   a) Obtaining a biological sample from a woman subject undergoing        a change in hormone levels;    -   b) Determining from said sample the genotype of single        nucleotide polymorphisms rs1799853 (SEQ ID NO:1), rs4379368 (SEQ        ID NO:2), rs6025 (SEQ ID NO:3), rs1799963 (SEQ ID NO:4),        rs8176719 (SEQ ID NO:5), rs8176750 (SEQ ID NO:6), rs9574 (SEQ ID        NO:7), rs2289252 (SEQ ID NO:8), and rs710446 (SEQ ID NO:9);    -   c) Determining the clinical risk factors of said woman subject,        said clinical risk factors are selected from the group        comprising the smoking status, the BMI, the age, the familial        history of blood clotting diseases and the change in hormone        levels;    -   d) Combining the genotyping data of step b) and the clinical        risk factors of step c) on a decision support algorithm that        gives a risk score; and    -   e) Analysing the risk score in order to determine the risk of        said woman subject to develop a blood clotting disease; and    -   f) Administering an effective amount of a compound adapted to        the prevention of a blood clotting disease to said subject when        the risk of developping a blood clotting disease is confirmed.

Compounds adapted to the prevention of a blood clotting disease areselected from the group comprising Apixaban, Rivaroxaban, Dabigatran,Edoxaban, heparin, vitamin K antagonists and coumarin drugs such asWarfarin.

The present invention relates also to a method of treatment of a womansubject undergoing a change in hormone levels having a risk ofdeveloping a blood clotting disease, the method comprising:

-   -   a) Obtaining a biological sample from a woman subject undergoing        a change in hormone levels;    -   b) Determining from said sample the genotype of single        nucleotide polymorphisms rs1799853 (SEQ ID NO:1), rs4379368 (SEQ        ID NO:2), rs6025 (SEQ ID NO:3), rs1799963 (SEQ ID NO:4),        rs8176719 (SEQ ID NO:5), rs8176750 (SEQ ID NO:6), rs9574 (SEQ ID        NO:7), rs2289252 (SEQ ID NO:8), and rs710446 (SEQ ID NO:9);    -   c) Determining the clinical risk factors of said woman subject,        said clinical risk factors are selected from the group        comprising the smoking status, the BMI, the age, the familial        history of blood clotting diseases and the change in hormone        levels;    -   d) Combining the genotyping data of step b) and the clinical        risk factors of step c) on a decision support algorithm that        gives a risk score;    -   e) Analysing the risk score in order to determine the risk of        said woman subject to develop a blood clotting disease; and    -   f) Administering an effective amount of a compound adapted to        the treatment of a blood clotting disease to said subject when        the risk of developping a blood clotting disease is confirmed.

Compounds adapted to the treatment of a blood clotting disease areselected from the group comprising Apixaban, Rivaroxaban, Dabigatran,Edoxaban, heparin, vitamin K antagonists and coumarin drugs such asWarfarin.

Those skilled in the art will appreciate that the invention describedherein is susceptible to variations and modifications other than thosespecifically described. It is to be understood that the inventionincludes all such variations and modifications without departing fromthe spirit or essential characteristics thereof. The invention alsoincludes all of the steps, features, compositions and compounds referredto or indicated in this specification, individually or collectively, andany and all combinations or any two or more of said steps or features.The present disclosure is therefore to be considered as in all aspectsillustrated and not restrictive, the scope of the invention beingindicated by the appended claims, and all changes which come within themeaning and range of equivalency are intended to be embraced therein.

Various references are cited throughout this specification, each ofwhich is incorporated herein by reference in its entirety.

The foregoing description will be more fully understood with referenceto the following Examples. Such examples are however exemplary ofmethods of practising the present invention and are not intended tolimit the scope of the invention.

EXAMPLE Example 1 Performance of the Prognostic Method

Characteristics of the Studied Population

This study involved human subjects and was carried out in accordancewith the tenets of the Declaration of Helsinki; all participants signedan informed consent. The study includes 794 female cases who havedeveloped at least one episode of VTE while taking CC. These cases arepart of the previously described PILl Genetic RIsk Monitoring (PILGRIM)study (Suchon P, et al. (2017) Clin Genet; 91(1):131-36), which relatesthe method used to confirm the occurence of thrombosis. 828 controlwomen were also collected from different sources: 523 are part of thePILGRIM study; 174 are part of the CoLaus study (Firmann M, et al.(2008) BMC Cardiovasc Disord;8:6), 56 were recruited between 1997 and1998 in south of France and the remaining controls were recruitedbetween 2012 and 2016 among Swiss population. These control women aretaking CC but have not developed VTE by the time of the genotypinginvestigation.

Among 1622 women taking an oral contraceptive pill, 794 have developed athrombotic event, either a deep vein thrombosis (DVT) or a pulmonaryembolism (PE). Distribution of age, BMI and smoking status are presentedin both populations (Tables 5 and 6). Age ditribution is similar in bothgroups; BMI and smoking status are slightly higher in cases.

TABLE 5 Clinical characteristics (mean) Subjects (n = 794) Controls (n =828) Age (years) 32.3 31.5 Body Mass Index (kg/m2) 24 22.3 Smokingstatus 260 206

TABLE 6 Cases (n) Controls (n) Total number 794 828 VTE 794 DVT 600 PE194 Age (mean) 32 31.5 BMI (mean) 24 23 Family history of 222 19 VTESmoking 260 206

Genetic Determinants of Thrombotic Events (DVT+PE)

50 genetic polymorphisms were identified for all 1622 women in the studyusing Illumina's Veracode-BeadXpress technology. The selection of theseSNPs was made according to the meta-analyses of the existing literatureand the smaller-scale laboratory studies that we carried. In moredetails, SNPs were genotyped using Illumina GoldenGate technology andassessed using Illumina BeadXpress and GenomeStudio V2011.1 software.Clusters for each SNP were curated manually and undetermined sampleswere further genotyped using Sanger sequencing. SNP rs1053878 wasgenotyped using RFLP-PCR; in more details, the DNA region was amplifiedwith the following primers (Forward: 5′-GCCACCGTGTCCACTACTATG-3′ andReverse: 5′-GTCCACGCACACCAGGTAAT-3′) and the amplicons were digestedwith PvuII restriction enzyme. Controls from the CoLaus cohort werepreviously genotyped as described (Kutalik Z, et al. (2011)Biostatistics;12(1):1-17). For the CoLaus controls, proxys (r2>85%) wereused for 9 SNPs (rs4572916 for rs10029715, rs8176704 for rs1053878,rs3736455 for rs13146272, rs6018 for rs1800595, rs4253417 for rs2289252,rs11038993 for rs3136516, rs2169682 for rs7082872, rs687621 forrs8176719 and rs2069952 for rs9574).

The following steps were performed on the cohort

-   -   (i) The cohort was randomly divided into a training set and a        test set (out-of-sample approach).    -   (ii) a stepwise selection (AIC, Akaike information criterion)        and a logistic regression using all variables (genetic and        clinical variables) was performed to select variables and assign        coefficients for each clinical variable and each SNP.    -   (iii) The fitted model was applied to the test set to compute        predictions.    -   (iv) The predictions and thrombosis state of the women in the        test set were stored.

This process was repeated 10′000 times (“runs”). Each run selected anumber of variables as significant. When the variable was not selected,the coefficient was set to 0. Then the median of the coefficients foreach variable over the 10′000 runs was calculated. All variables thathad a non-zero median were selected in the final model.

Logistic regression models were fitted step-wise to find the optimalmultivariate model in the 10,000 training sets. By averaging these10,000 models, 4 clinical variables were identified as risk factorscontributing to the prediction of the risk of VTE in our population.Age, BMI, smoking status and family history were selected and hadsignificant p-values (Table 7). 9 out of the 46 tested SNPs were in theaveraged model and also significantly associated with the development ofthrombosis (Table 7).

This approach selected 9 SNPs and 4 clinical factors (Table 7). Thep-value and Odd-Ratio (OR) indicate the significance and the strength,respectively, of the association between a SNP and the development ofthrombosis.

TABLE 7 Factor Gene Change p-value¹ OR² rs6025 (SEQ ID Factor V gene G >A  8.3 × 10⁻¹⁴ 6.46 NO: 3) (NG_011806) rs1799963 (SEQ Factor II gene G >A 3.7 × 10⁻⁸ 5.32 ID NO: 4) (NG_008953) rs8176719 (SEQ ABO gene − > G6.0 × 10⁻⁷ 1.52 ID NO: 5) (NG_006669) rs8176750 (SEQ ABO gene C > − 2.5× 10⁻³ 0.59 ID NO: 6) (NG_006669) rs9574 (SEQ ID PROCR gene C > G  9 ×10⁻⁴ 1.25 NO: 7) (NG_032899) rs2289252 (SEQ Factor 11 gene C > T 2.9 ×10⁻⁴ 1.34 ID NO: 8) (NG_008051) rs710446 (SEQ KNG1 gene A > G 3.8 × 10⁻²1.22 ID NO: 9) (NG_016009) rs1799853 (SEQ CYP2C9 gene C > T 5.0 × 10⁻⁴1.55 ID NO: 1) (NG_008385) rs4379368 (SEQ SUGCT gene C > T 3.6 × 10⁻²1.35 ID NO: 2) (NG_023422) age  4 × 10⁻² 1.01 Smoking status 1.5 × 10⁻⁴1.63 BMI 8.9 × 10⁻⁷ 1.07 Familial history 2.4 × ⁻⁷  2.13 ¹p-valuesobtained from the cohort of 1622 women and a logistic regression on thewhole population ²Odd-Ratio obtained from the cohort of 1622 women and alogistic regression in an out-of-sample approach

The Odd-Ratio (OR) quantifies the association between two parameters ina given population (Cornfield J., (1951). Journal of the National CancerInstitute. 11: 1269-1275). These values are obtained from mathematicalmodels such as the logistic regression used herein. A logisticregression is a mathematical model that measures the relationshipbetween variables by predicting the probability of a given outcome(Walker, S. H.; Duncan, D. B. (1967). Biometrika. 54: 167-178). The ORand p-values are the output of the logistic regression and correspond tothe strength and the significance of the measured relationshiprespectively. Different approaches can be used to generate the p-values.In table 6, the p-values have been determined from 10′000 repetitions ofa logistic regression using the whole 1622 women population whilst otherapproaches generate p-values using a logistic regression only on half ofthe population randomly selected 10′000 times in an out-of-samplemanner.

FIG. 1 represents the distribution of the scores Pill Protect® (PP)across the controls represented by subjects who did not developed DVTand/or PE (wo TEV) and the subjects that developed DVT and/or PE (withTEV). The distribution is shown as a boxplot, where the thick line inthe box is the median (second quartile), the bottom of the box is thefirst quartile, the top of the box is the third quartile, the whiskersrepresent the last point before outliers.

As shown on FIG. 1, the difference between the score distribution of thesubjects and controls is statistically significant (p-value=10⁻³). Thescores of the controls range from 1.9 to 1891 with a mean of 22 and amedian of 12 while the scores of the subjects range from 3.5 to 17′419with a mean of 97 and a median of 24.

Efficiency and Specificity of the Test

The following algorithm:

PP=exp(β₀+β₁ x ₁+ . . . +β_(n) x _(n))/exp(β₀+β₁ x _(1st)+ . . . +β_(n)x _(nst))

was then applied to the test set and the predictions were plotted usinga ROC curve to measure the efficiency of the test (AUC=71% −algorithmPP). In comparison, similar algorithms using only the clinical variableswith coefficients generated from the literature (AUC=61%−algorithm MD)or using the clinical variables and Factor V and Factor II withcoefficients generated from the literature (AUC=65%−algorithm MDg), werealso used and the predictions computed into a ROC curve. The MDalgorithm is the most representative of the medical questionnairecurrently used by physician to estimate the risk of DVT, whilst the MDgis the most representative of the medical questionnaire currently usedplus genetic testing that are currently available (Factor V and FactorII only). The three ROC curves are shown in FIG. 2.

In FIG. 2, the top curve (full line) represents the most efficient testand corresponds to the algorithm PP. The two other ROC curves (straightdashed and dotted lines) correspond to estimated values generated bymeta-analysis of the literature as follows:

MD=bmi*smo*famh;

-   -   bmi=2.31 when patient's BMI is ≥30 or 1.43 when patient's BMI is        ≥25    -   smo=1 if the patient is non-smoker or 1.6 if the patient is        smoker    -   famh=1 if the patient has no familial history of thrombosis        event or 2 if the patient has a familial history of thrombosis        event.

MDg =bmi*smo*famh*FV*FII:

-   -   bmi, smo and famh as stated above    -   FV=rs6025=1 if the patient is wt for rs6025, 4.11 if the patient        is heterozygous for rs6025 or 11.15 if the patient is homozygous        for the effect allele of rs6025    -   FII=rs1799963=1 if the patient is wt for rs1799963, 3.5 if the        patient is heterozygous for rs1799963 or 8.4 if the patient is        homozygous for the effect allele of rs1799963

A ROC (Receiver Operating Characteristic) curve is established andmeasures how well the different models discriminate the women at riskfrom the women without risk. The true positive rate (TPR) is plottedagainst the false positive rate (FPR) at various threshold settings(Fawcett T., (2006), Pattern Recognition Letters., 27: 861-874).

The Area Under the Curve (AUC) is the probability that a positive testranks higher than a negative test in order to discriminate the women atrisk to the women without risk. The AUC (Area Under the Curve) rangesfrom 0.5 (50%−no predictive value) to 1 (100%−perfect discrimination;Fawcett T., (2006), Pattern Recognition Letters., 27: 861-874). Thereare several ways to calculate AUC, either using the whole 1622 womenpopulation or using only the test set that corresponds to half of thepopulation that was not used to select variables. The AUC presented inthe text above have been calculated using the whole 1622 womenpopulation.

These results demonstrate that the score Pill Protect® detects morewomen at risk than that of the current standard of practice with orwithout genetic testing.

Table 8 discloses the AUC of three different models. The genetic scoredescribed by De Haan et al. (de Haan H G, et al. (2012) Blood;120(3):656-63) is based on 5 SNPs (rs6025, rs1799963, rs8176719,rs2066865, rs2036914). 3 of these SNPs are in common with the finalmodel. Applying this 5 SNPs model to the half of the population of 1622women yielded an AUC of 0.64 (0.62-0.68), which is less than thedescribed AUC on MEGA and LETS cohorts (0.69 and 0.67 respectively) dueto winners curse.

The genetic score described by Bruzelius et al. (Bruzelius M, et al.(2015) J Thromb Haemost;13(2):219-27) is based on 7 SNPs (rs6025,rs1799963, rs514659, rs2289252, rs1799810, rs710446, rs2066865) and 4interactions. The genotyping data for one SNP is not known among these 7SNPs (rs1799810) and it was, therefore, not used in the comparison. Thegenetic score associated with this set of six SNPs reaches an AUC of0.65 (0.63-0.68) in the present study which is very similar to what wasdescribed by Bruzelius et al. (0.66; [0.64-0.68]). Still both AUC valuesare significantly below the 0.68 AUC of the 9 SNPs Pill Protect® model.The AUC presented in table 8 have been calculated using half of thepopulation.

TABLE 8 Model AUC 95% CI Pill Protect ® model 0.71 0.69-0.74 Bruzeliusgenetics 0.65 0.63-0.68 De Haan genetics 0.64 0.62-0.68 CI: confidenceinterval

As represented by the ROC curves, the risk estimated from Pill Protect®model improves the performance to determine whether a woman is at riskof developing DVT or PE.

Example 2 Determination of the Risk for Women Under HormonalContraceptives

Table 9 discloses the genotype and clinical parameters of women underhormonal contraceptives. The genotype of SEQ ID NO:1 to SEQ ID NO:9 andvarious clinical parameters have been determined as follows:

For each patient, the family history, the BMI, the age, the smokingstatus, and the genotype of SEQ ID NO:1 to SEQ ID NO:9 has beenindicated by values as follows:

Family history: 0=no family history of blood clotting disease, 1=familyhistory (first grade) of blood clotting disease.

Smoking status: 0 corresponds to a non-smoking subject and 1 correspondsto a smoking subject.

Value of SEQ ID NO:1 to SEQ ID NO:9 is 2=homozygous for the effectallele, 1=heterozygous for the effect allele, 0=homozygous for thenon-effect allele.

The development of a deep vein thrombosis (DVT) or a pulmonary embolism(PE) is indicated for each patient.

TABLE 9 Clinical Parameters SEQ ID NO: Family Patient 1 2 3 4 5 6 7 8 9History BMI Age Smoking 1 1 0 0 1 1 0 1 2 2 1 22.79 34 1 2 1 1 1 0 1 0 12 2 0 20.45 15 0 3 1 1 1 0 1 0 0 0 2 0 20.42 21 1 4 1 0 1 0 1 0 0 1 2 027.92 48 0 5 0 1 1 0 2 0 1 1 1 0 23.05 24 1 6 1 0 0 1 2 0 0 1 2 0 20.3138 0 7 0 0 1 0 1 0 0 2 2 0 21.3 19 0 8 2 0 1 0 1 0 1 1 1 0 20.83 29 0 90 1 1 0 1 0 0 1 1 0 28.93 22 1 10 1 1 1 0 1 0 0 1 1 0 18.87 19 1 11 1 10 1 1 0 1 1 1 1 20.76 16 0 12 2 0 0 1 1 0 2 2 1 0 24.14 19 0 13 0 1 1 00 0 0 0 0 0 21.97 25 1 14 0 0 1 0 1 0 1 0 1 0 21.8 19 1 15 0 0 0 1 1 1 10 0 0 22.955 42 0 16 0 0 0 1 1 0 1 1 0 0 22.463 42 0 17 0 0 0 0 0 0 1 20 1 16.9 29 1 18 0 1 0 0 0 0 1 0 0 1 19.72 20 0 19 0 0 0 0 0 0 1 1 0 118.37 27 1 20 0 0 0 0 1 0 1 0 0 1 19.53 20 0 21 0 0 0 0 0 0 0 0 1 1 20.423 1

For each patient, the following scores were calculated and reported intable 10.

Pill Protect® score (PP) calculated according to the formula describedin the present invention taking into account genotyping data andclinical risk factors:

PP=exp(β₀+β₁ x ₁+ . . . +β_(n) x _(n))/exp(β₀+β₁ x _(1st)+ . . . +β_(n)x _(nst))

Absolute risk (AR) calculated according to the formula described in thepresent invention taking into account the incidence of the disease:

AR=adjPP*the incidence according to the subject's age

DH score is a genetic score calculated as described in De Haan et al.based on rs6025, rs1799963, rs8176719, rs2066865, and rs2036914.

MD score is the current standard of practice without genetic testing andis calculated as described in the present invention:

MD=bmi*smo*famh;

-   -   bmi=2.31 when patient's BMI is ≥30 or 1.43 when patient's BMI is        ≥25    -   smo=1 if the patient is non-smoker or 1.6 if the patient is        smoker    -   famh=1 if the patient has no familial history of thrombosis        event or 2 if the patient has a familial history of thrombosis        event.

MDg score is the current standard of practice with genetic testing andis calculated as described in the present invention:

MDg =bmi*smo*famh*FV*FII:

-   -   bmi, smo and famh as stated above    -   FV=rs6025=1 if the patient is wt for rs6025, 4.11 if the patient        is heterozygous for rs6025 or 11.15 if the patient is homozygous        for the effect allele of rs6025    -   FII=rs1799963=1 if the patient is wt for rs1799963, 3.5 if the        patient is heterozygous for rs1799963 or 8.4 if the patient is        homozygous for the effect allele of rs1799963.

TABLE 10 Score Patient PP AR DH MD MDg DVT/PE 1 107.88 306 5.14 2.007.00 PE 2 34.25 43 7.01 1.00 4.11 DVT 3 42.09 82 19.06 1.00 4.11 DVT 459.12 267.2 10.94 2.29 9.40 DVT 5 45.21 102 17.12 1.00 4.11 DVT 6 39.85129 14.84 1.00 3.50 DVT 7 23.02 40 7.01 1.00 4.11 PE 8 29.48 76 7.011.00 4.11 DVT 9 52.90 108 10.94 1.43 5.88 PE 10 40.77 70 9.26 1.00 4.11DVT 11 38.26 52 6.79 2.00 7.00 PE 12 28.19 49 5.14 1.00 3.50 DVT 1314.15 33 5.00 1 4.11 none 14 14.22 24 9.26 1 4.11 none 15 5.12 19 5.14 13.5 none 16 11.13 41 6.79 1 3.5 none 17 3.75 10 1.00 2 2 none 18 1.86 31.56 2 2 none 19 3.01 7 2.06 2 2 none 20 2.08 4 3.22 2 2 none 21 3.71 81.74 2 2 none

In the present invention, a score PP>20 is indicative that a womansubject has a risk as high as the natural risk during postpartum periodof having or developing a blood clotting disease. Table 10 shows thatpatients No. 1-12 with a Pill Protect® score PP>20 have developed a DVTor PE. Women with a Pill Protect® score PP<20 have not developed a DVTnor a PE so far. All of these women were taking an oral contraceptive.The current standard approach MD or MDg score does not discriminatewomen at risk from women not at risk as can be seen from similar resultsin patients No. 1-12 and patients No. 13-21. The DH score (DeHaan) doesnot discriminate neither the women at risk (low score (<20) for bothcases and controls). On the other hand, the Pill Protect® score givessystematically a high score (PP>20) in all patients that have developeda DVT or PE compared to patients that have not developed a bloodclotting disease.

Example 3 Determination of the Risk for Women Under Hormone ReplacementTherapy

Characteristics of the Studied Population

Among 26 women above 45 years old and qualified to take hormonereplacement therapy, 11 have developed a thrombotic event, either a deepvein thrombosis (DVT) or a pulmonary embolism (PE). Distribution of age,BMI and smoking status are presented in both populations (Table 11). Agedistribution is similar in both groups, BMI and Smoking status isslightly higher in subjects as expected, because they are known riskfactors. However the BMI distribution is not so different that it willhide genetic factors.

TABLE 11 Clinical characteristics (mean) Subjects (n = 11) Controls (n =15) Age (years) 53.7 49.6 Body Mass Index (kg/m2) 25.5 22 Smoking status3 2

FIG. 3 represents the distribution of the scores Pill Protect® (PP)across the controls subjects who did not develop DVT and/or PE (wo TEV)and the subjects that developed DVT and/or PE (with TEV) under orplanning hormone replacement therapy. The distribution is shown as aboxplot, where the thick line in the box is the median (secondquartile), the bottom of the box is the first quartile, the top of thebox is the third quartile, the whiskers represent the last point beforeoutliers.

The difference between the score distribution of the subjects andcontrols is dependent on the number of women (N=26, p-value=0.044). Thescores of the controls range from 1.52 to 5.8 with a mean of 3.8 of anda median of 3.8 while the scores of the subjects range from 2.48 to 43with a mean of 11.8 and a median of 7.3.

Performance of the Test

As shown in table 12, when compared to the current standard of care (themedical questionnaire), the following performances of the test wereobtained:

TABLE 12 Algorithm PP score MD score True Positive 6 subjects 3 subjectswomen (TP) False Positive 0 control 1 control women (FP) True Negative15 controls 14 controls women (TN) False Negative 5 subjects 8 subjectswomen (FN) PPV 100% 75% NPV  75% 64% specificity 100% 93% sensitivity 54% 27%

In table 12, “True Positive women” refers to the subjects that havedeveloped a DVT and/or PE and have a PP score equal or above 7 or a MDscore equal or above 3;

“False Positive women” are the controls subjects that have not developeda DVT and/or PE and have a PP score equal or above 7 or a MD score equalor above 3;

“True Negative women” are the controls subjects that have not developeda DVT and/or PE and have a PP score below 7 or a MD score below 3; and

“False Negative women” are the subjects that have developed a DVT and/orPE and have a PP score below 7 or a MD score below 3.

The term “PPV” refers to “Positive Predicted Value”. It represents thepercentage of women with a positive test result who truly have developedthe DVT and/or PE and is calculated by the formula PPV=TP/(TP+FP).

“NPV” refers to “Negative Predicted Value”. It represents the percentageof women with a negative test result who did not develop the DVT and/orPE and is calculated by the formula TN/(TN+FN).

“Specificity” is calculated by the formula TN/(TN+FP) and the“Sensitivity” by the formula TP/(TP+FN).

A threshold of 7 was chosen for the PP algorithm When taking intoaccount the elevated incidence in women over 50 years old, which is 2 to4 times higher than women between 20 and 50 years old, it corresponds tothe highest natural risk of a woman's lifetime that is in postpartum(20).

A threshold of 3 was chosen for the MD algorithm because it correspondsto a combination of at least 2 of the 3 clinical variables.

The performance of the Pill Protect® (PP) test is higher than thecurrent standard of care score) for the PPV, NPV, specificity andsensitivity (Table 12).

1. A prognostic method for identifying if a woman subject undergoing achange in hormone levels is at risk of developing a blood clottingdisease, the method comprising the steps of: a) Determining in a samplefrom said woman subject the genotype of single nucleotide polymorphismof rs1799853 (SEQ ID NO:1), rs4379368 (SEQ ID NO:2), rs6025 (SEQ IDNO:3), rs1799963 (SEQ ID NO:4), rs8176719 (SEQ ID NO:5), rs8176750 (SEQID NO:6), rs9574 (SEQ ID NO:7), rs2289252 (SEQ ID NO:8), and rs710446(SEQ ID NO:9); b) Determining the clinical risk factors of said womansubject, said clinical risk factors are selected from the groupcomprising the smoking status, the BMI, the age, the familial history ofblood clotting diseases and the change in hormone levels; c) Combiningthe genotyping data of step a) and the clinical risk factors of step b)on a decision support algorithm that gives a risk score; and d)Analysing the risk score in order to determine the risk of said womansubject to develop a blood clotting disease.
 2. The prognostic methodaccording to claim 1, wherein said woman subject undergoing a change inhormone levels is having a contraceptive, a combined contraceptive, ahormonal replacement therapy, a progestin-only contraceptive, ispregnant, is having assisted reproductive technology, or is inpostpartum period.
 3. The prognostic method according to claim 1,wherein said blood clotting diseases is selected from the groupcomprising deep vein thrombosis, pulmonary embolism, vein thrombosis andarterial thrombosis.
 4. The prognostic method according to claim 1,wherein the genotype of single nucleotide polymorphism is determined bynucleic acid sequencing and/or by PCR analysis.
 5. An apparatus forcalculating an estimation value of the risk of developing a bloodclotting disease in a woman subject undergoing a change in hormonelevels based on the woman subject-specific input features, saidapparatus comprising: a) a data interface for receiving said inputfeatures; b) a processor for calculating said estimation value byapplying a decision support algorithm as a function of numerical valuesderived from said received input features; and c) a user interface foroutputting said estimation value; wherein said input features include acombination of: (i) the genotype of single nucleotide polymorphism ofrs1799853 (SEQ ID NO:1), rs4379368 (SEQ ID NO:2), rs6025 (SEQ ID NO:3),rs1799963 (SEQ ID NO:4), rs8176719 (SEQ ID NO:5), rs8176750 (SEQ IDNO:6), rs9574 (SEQ ID NO:7), rs2289252 (SEQ ID NO:8), and rs710446 (SEQID NO:9); and (ii) the clinical risk factors comprising the smokingstatus, the BMI, the age, the familial history of blood clottingdiseases and the change in hormone levels of said woman subject.
 6. Amethod for calculating an estimation value of the risk of developing ablood clotting disease in a woman subject undergoing a change in hormonelevels based on woman subject-specific input features, said methodcomprising: a) selecting said input features to include a combinationof: (i) the genotype of single nucleotide polymorphism of rs1799853 (SEQID NO:1), rs4379368 (SEQ ID NO:2), rs6025 (SEQ ID NO:3), rs1799963 (SEQID NO:4), rs8176719 (SEQ ID NO:5), rs8176750 (SEQ ID NO:6), rs9574 (SEQID NO:7), rs2289252 (SEQ ID NO:8), and rs710446 (SEQ ID NO:9); and (ii)the clinical risk factors comprising the smoking status, the BMI, theage, the familial history of blood clotting diseases and the change inhormone levels of said woman patient; and b) calculating said estimationvalue by applying a decision support algorithm as a function ofnumerical values derived from said received input features.
 7. Themethod according to claim 6, further comprising optimizing said inputfeatures by a learning process based on a stored dataset of a pluralityof woman subjects so as to minimize a prediction error.
 8. A kit for usein identifying if a woman subject undergoing a change in hormone levelsis having a risk of developing a blood clotting disease, said kitcomprising i) detection reagents for detecting the genotype of singlenucleotide polymorphism of rs1799853 (SEQ ID NO:1), rs4379368 (SEQ IDNO:2), rs6025 (SEQ ID NO:3), rs1799963 (SEQ ID NO:4), rs8176719 (SEQ IDNO:5), rs8176750 (SEQ ID NO:6), rs9574 (SEQ ID NO:7), rs2289252 (SEQ IDNO:8), and rs710446 (SEQ ID NO:9); and ii) optionally instructions foruse.
 9. The kit for use according to claim 8, wherein the singlenucleotide polymorphism detection reagents are isolated or synthetic DNAoligonucleotide probes or primers, or RNA oligonucleotides or primers orPNA oligomers or a combination thereof, that hybridize to a fragment ofa target nucleic acid molecule containing one of the single nucleotidepolymorphisms specified in any one of SEQ ID NO: 1 to 9, or a complementthereof.
 10. The kit for use according to claim 8, wherein said singlenucleotide polymorphism detection reagents can differentiate betweennucleic acids having a particular nucleotide at a target singlenucleotide polymorphism position.
 11. The kit for use according to claim8, wherein the single nucleotide polymorphism detection reagentshybridize under stringent conditions to at least 8, 10, 12, 16, 18, 20,22, 25, 30, 40, 50, 55, 60, 65, 70, 80, 90, 100, 120 or more consecutivenucleotides in a target nucleic acid molecule comprising the singlenucleotide polymorphisms specified in any one of SEQ ID NO: 1 to 9, or acomplement thereof.
 12. The kit for use according to claim 8, whereinthe single nucleotide polymorphism detection reagents areoligonucleotides or primers having a length of at least 8 nucleotides,preferably a length of at least 10, 12, 16, 17, 18, 19, 20, 21, 22, 23,24 or 25 nucleotides.
 13. The kit for use according to claim 8, whereinthe single nucleotide polymorphism detection reagents are 6abeledcompounds.